Search Results for author:
Found 16 papers, 3 papers with code
Learning Stage-wise GANs for Whistle Extraction in Time-Frequency Spectrograms
To overcome this limitation, we present a framework of stage-wise generative adversarial networks (GANs), which compile new whistle data suitable for deep model training via three stages: generation of background noise in the spectrogram, generation of whistle contours, and generation of whistle signals.
Learning Knowledge-Rich Sequential Model for Planar Homography Estimation in Aerial Video
To address this concern, we develop a sequential estimator that directly processes a sequence of video frames and estimates their pairwise planar homographic transformations in batches.
A Hamiltonian Monte Carlo Method for Probabilistic Adversarial Attack and Learning
Although deep convolutional neural networks (CNNs) have demonstrated remarkable performance on multiple computer vision tasks, researches on adversarial learning have shown that deep models are vulnerable to adversarial examples, which are crafted by adding visually imperceptible perturbations to the input images.
Learning Pose Grammar for Monocular 3D Pose Estimation
In this paper, we propose a pose grammar to tackle the problem of 3D human pose estimation from a monocular RGB image.
Ranked #15 on
3D Human Pose Estimation
on HumanEva-I
Discrete Optimal Graph Clustering
2) the relaxing process of cluster labels may cause significant information loss.
Neural Task Planning with And-Or Graph Representations
This paper focuses on semantic task planning, i. e., predicting a sequence of actions toward accomplishing a specific task under a certain scene, which is a new problem in computer vision research.
Learning Pose Grammar to Encode Human Body Configuration for 3D Pose Estimation
In this paper, we propose a pose grammar to tackle the problem of 3D human pose estimation.
Ranked #1 on
3D Absolute Human Pose Estimation
on Human3.6M
(Average MPJPE (mm) metric)
A Causal And-Or Graph Model for Visibility Fluent Reasoning in Tracking Interacting Objects
We introduce a Causal And-Or Graph (C-AOG) to represent the causal-effect relations between an object's visibility fluent and its activities, and develop a probabilistic graph model to jointly reason the visibility fluent change (e. g., from visible to invisible) and track humans in videos.
Discrete Multi-modal Hashing with Canonical Views for Robust Mobile Landmark Search
Finally, compact binary codes are learned on intermediate representation within a tailored discrete binary embedding model which preserves visual relations of images measured with canonical views and removes the involved noises.
Multi-View People Tracking via Hierarchical Trajectory Composition
This paper presents a hierarchical composition approach for multi-view object tracking.
Geometric Scene Parsing with Hierarchical LSTM
This paper addresses the problem of geometric scene parsing, i. e. simultaneously labeling geometric surfaces (e. g. sky, ground and vertical plane) and determining the interaction relations (e. g. layering, supporting, siding and affinity) between main regions.
Human Re-identification by Matching Compositional Template with Cluster Sampling
This paper aims at a newly raising task in visual surveillance: re-identifying people at a distance by matching body information, given several reference examples.
Detect What You Can: Detecting and Representing Objects using Holistic Models and Body Parts
Our model automatically decouples the holistic object or body parts from the model when they are hard to detect.
The Role of Context for Object Detection and Semantic Segmentation in the Wild
In this paper we study the role of context in existing state-of-the-art detection and segmentation approaches.
Single-View 3D Scene Parsing by Attributed Grammar
The grammar takes superpixels as its terminal nodes and use five production rules to generate the scene into a hierarchical parse graph.
Robust Region Grouping via Internal Patch Statistics
In this work, we present an efficient multi-scale low-rank representation for image segmentation.
